Author ORCID Identifier

https://orcid.org/0000-0003-4275-9835

Date Available

8-22-2025

Year of Publication

2025

Document Type

Doctoral Dissertation

Degree Name

Doctor of Philosophy (PhD)

College

Arts and Sciences

Department/School/Program

Earth and Environmental Sciences (Geology)

Faculty

Dr. Alan E. Fryar

Faculty

Dr. Andrea M. Erhardt

Abstract

Karst aquifers represent some of the world’s most productive yet vulnerable groundwater systems, supplying freshwater to approximately 10% of the global population across 15% of Earth’s ice-free land surface. Their distinctive hydrological characteristics— including rapid recharge, preferential flow paths, and limited natural filtration— create unique vulnerabilities to both climate variability and contamination that pose significant challenges for sustainable water resource management. This dissertation presents a comprehensive multi-scale analysis of karst aquifer vulnerability through three interconnected studies that examine climate change impacts, microbial contamination dynamics, and contaminant transport processes across diverse geographic and hydrogeologic settings.

Chapter two investigates climate change impacts on hydrology of the Martandnag spring in the Liddar catchment in the Kashmir valley of northern India. Statistical time series (autocorrelation and cross-correlation) and machine-learning (ML) techniques (random forest regression (RFR) and support vector regression (SVR)) were used to characterize how rainfall, temperature, and snow cover affect the karst spring flow. Future responses of the spring stage were predicted based on climate scenarios in the Intergovernmental Panel on Climate Change Assessment Report 6. The statistical time series showed that the memory effect of Martandnag spring varies from 43 to 61 days, indicating moderate karstification and a relatively high storage capacity of the karst aquifer in the Liddar catchment. The delay between recharge and discharge varies from 13 to 44 days, and it is more strongly correlated to snow/ice melt than to rainfall. The ML analysis shows that SVR outperformed RFR in predicting spring flow. Under all climate scenarios, a trained SVR model showed that spring flow increased from late winter to early spring and decreased during summer (except in August) and autumn. Scenarios with increased greenhouse gas emissions further reduced flow in summer and autumn. These predictions can be helpful for water-resource planning in similar watersheds in the Western Himalayas.

Chapter three focuses on the karstic Royal Spring basin in Kentucky, encompassing urban and agricultural land uses. The city of Georgetown distributes treated water from Royal Spring to over 33,000 customers. E. coli dynamics at Royal Spring were examined from June 2021 through June 2022, with variability assessed under wet versus dry weather conditions. Quantitative microbial risk assessment (QMRA) was also used to estimate potential health risks from the pathogenic bacterial strain E. coli O157:H7. E. coli concentrations in weekly water samples varied from 12 to 1,732.8 MPN/100 mL, with a geometric mean of 117.2 MPN/100 mL. The mean concentration in wet periods was approximately double that during dry conditions. Because the pathogen was not detected by quantitative PCR (qPCR), QMRA was conducted based on literature data for water treatment plant operations (occupational) and recreational activities near the spring. The median probability of annual infection was 5.11×10−3 for occupational exposure and 1.45×10−2 for recreational exposure. Uncertainty and sensitivity analyses revealed that health risks were most sensitive to the pathogen/E. coli ratio and ingestion rate. Although the pathogen was not detected by qPCR, the presence of E. coli suggests potential fecal contamination. This highlights the importance of continued monitoring and investigation of different detection methods to better understand potential health risks in karst systems.

Chapter four applies a systematic empirical framework—incorporating parameter optimization, model selection based on Akaike Information Criterion, and bootstrap uncertainty analysis—to multi-tracer transport data from the Royal Spring basin in central Kentucky, USA. Four tracers with varying surface properties, including rhodamine WT dye (RWT), 1-m latex microspheres, and two genetically distinct E. coli strains with low (LAI) and high (HAI) attachment efficiencies, were introduced into the karst conduit network under baseflow conditions. Breakthrough curves collected at two monitoring sites (a well ∼ 750 m downgradient and Royal Spring, ∼ 6.25 km from the injection site) were modeled using five candidate functions. Results show that transport dynamics were strongly tracer-specific: two-component models captured dual-pathway behavior for RWT and microspheres (R2 = 0.59–0.95), while LAI was consistently modeled by a lognormal function. HAI exhibited extended retention with mean transit times up to 597 h, whereas LAI and microspheres arrived earlier (∼ 70 h). The limited detection data (n = 2) precludes robust estimation. The systematic model selection revealed clear patterns linking tracer surface properties to transport complexity, with RWT and LAI captured by simpler models than microspheres and HAI. Spatial differences between sites revealed tracer-specific transport evolution, with increasing model complexity downstream reflecting the integration of multiple flow pathways. The rigorous empirical modeling can quantify tracer-specific transport differences in karst systems, highlighting the inadequacy of conventional solute tracers alone for microbial risk assessment.

Digital Object Identifier (DOI)

https://doi.org/10.13023/etd.2025.437

Funding Information

Chapter three is based upon work supported, in part, by the U.S. Geological Survey under Grant/Cooperative Agreement No. G21AP10631 through an award to A.E.F., D.M.B., and R.T.D. PCR analysis was supported by the National Institute of General Medical Services of the National Institutes of Health (NIH) under award number P20GM113117 to J.M.H. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the opinions or policies of the U.S. Geological Survey or NIH. Mention of trade names or commercial products does not constitute their endorsement by the U.S. Geological Survey or NIH. This work was funded in part by the Kentucky Geological Survey through a Commonwealth Research Assistantship to R.T.D.; by the University of Kentucky Martin-Gatton College of Agriculture, Food and Environment through a Kerri Casner Fellowship to R.T.D.; and by the Karst Waters Institute through a William Wilson Scholarship to R.T.D.

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